Abstract
One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.
Highlights
IntroductionOne of the most often used image segmentation techniques is multilevel thresholding
One of the most often used image segmentation techniques is multilevel thresholding.It is divided into two types: bi-level and multilevel [1,2]
The benchmark image data sets are presented in Figures 4 and 5
Summary
One of the most often used image segmentation techniques is multilevel thresholding. It is divided into two types: bi-level and multilevel [1,2]. Multilevel thresholding is used to separate complex images, which can generate several thresholds, such as tri-level or quad-level thresholds, which break pixels into several identical parts depending on size. When there are only two primary gray levels in an image, bi-level thresholding yields acceptable results; when it is expanded to multilevel thresholding, the main drawback is the time-consuming computation [5]. Bilevel thresholding cannot precisely find the optimum threshold, due to the slight variation between the target and the context of a complex image [6,7]
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